{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqRZsIzfyL3k"
      },
      "source": [
        "取得数据，并引入基本函数库。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdGXCGqkyNui",
        "outputId": "f6784bc0-ebd2-48f8-e166-6b53a8c36f84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-11-16 06:30:09--  http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat [following]\n",
            "--2024-11-16 06:30:10--  https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5953527 (5.7M)\n",
            "Saving to: ‘Indian_pines_corrected.mat’\n",
            "\n",
            "Indian_pines_correc 100%[===================>]   5.68M  1.91MB/s    in 3.0s    \n",
            "\n",
            "2024-11-16 06:30:13 (1.91 MB/s) - ‘Indian_pines_corrected.mat’ saved [5953527/5953527]\n",
            "\n",
            "URL transformed to HTTPS due to an HSTS policy\n",
            "--2024-11-16 06:30:13--  https://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1125 (1.1K)\n",
            "Saving to: ‘Indian_pines_gt.mat’\n",
            "\n",
            "Indian_pines_gt.mat 100%[===================>]   1.10K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-11-16 06:30:14 (79.7 MB/s) - ‘Indian_pines_gt.mat’ saved [1125/1125]\n",
            "\n",
            "Collecting spectral\n",
            "  Downloading spectral-0.23.1-py3-none-any.whl.metadata (1.3 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from spectral) (1.26.4)\n",
            "Downloading spectral-0.23.1-py3-none-any.whl (212 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.9/212.9 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        }
      ],
      "source": [
        "! wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
        "! wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
        "! pip install spectral"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M1xgG-HLySiv"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.io as sio\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\n",
        "import spectral\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "432MZBELyYBW"
      },
      "source": [
        "1. 定义 HybridSN 类（无SE机制、无softmax）"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eyIJwEvvyXod"
      },
      "outputs": [],
      "source": [
        "# Indian_pines数据集有16个种类，因此定义class_num=16\n",
        "class_num=16\n",
        "\n",
        "# 定义HybridSN\n",
        "class HybridSN(nn.Module):\n",
        "  def __init__(self, in_channels=1, out_channels=class_num):\n",
        "    super(HybridSN, self).__init__()\n",
        "    # 三维卷积层\n",
        "    self.conv3d = nn.Sequential(\n",
        "        nn.Conv3d(1,8,kernel_size=(7,3,3),stride=1,padding=0),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv3d(8,16,kernel_size=(5,3,3),stride=1,padding=0),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv3d(16,32,kernel_size=(3,3,3),stride=1,padding=0),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "\t# 二维卷积层\n",
        "    self.conv2d = nn.Sequential(\n",
        "        nn.Conv2d(576, 64, kernel_size=(3,3),stride=1,padding=0),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "    # 全连接层\n",
        "    self.fc = nn.Sequential(\n",
        "        nn.Linear(64 * 17 * 17, 256),\n",
        "        nn.ReLU(),\n",
        "        nn.Dropout(0.4),\n",
        "        nn.Linear(256, 128),\n",
        "        nn.ReLU(),\n",
        "        nn.Dropout(0.4),\n",
        "        nn.Linear(128, 16)\n",
        "    )\n",
        "    # 论文里有加softmax，但本次实验下loss下降特别慢，因此没有使用\n",
        "    # self.softmax = nn.Softmax(dim=1)\n",
        "\n",
        "  # 正向传播过程\n",
        "  def forward(self, t):\n",
        "    t = self.conv3d(t)\n",
        "    # 进行卷积降维\n",
        "    t = t.view(t.shape[0], t.shape[1]*t.shape[2], t.shape[3], t.shape[4])\n",
        "    t = self.conv2d(t)\n",
        "    # 进行卷积降维进行flatten\n",
        "    t = t.view(t.shape[0],-1)\n",
        "    t = self.fc(t)\n",
        "    # t = self.softmax(t)\n",
        "    return t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywl8xtA6ylik"
      },
      "source": [
        "读取并创建数据集"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Scq1cOuWyoVu",
        "outputId": "5732cb2d-70e0-4ced-9ee4-10b2651be449"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hyperspectral data shape:  (145, 145, 200)\n",
            "Label shape:  (145, 145)\n",
            "\n",
            "... ... PCA tranformation ... ...\n",
            "Data shape after PCA:  (145, 145, 30)\n",
            "\n",
            "... ... create data cubes ... ...\n",
            "Data cube X shape:  (10249, 25, 25, 30)\n",
            "Data cube y shape:  (10249,)\n",
            "\n",
            "... ... create train & test data ... ...\n",
            "Xtrain shape:  (1024, 25, 25, 30)\n",
            "Xtest  shape:  (9225, 25, 25, 30)\n",
            "before transpose: Xtrain shape:  (1024, 25, 25, 30, 1)\n",
            "before transpose: Xtest  shape:  (9225, 25, 25, 30, 1)\n",
            "after transpose: Xtrain shape:  (1024, 1, 30, 25, 25)\n",
            "after transpose: Xtest  shape:  (9225, 1, 30, 25, 25)\n"
          ]
        }
      ],
      "source": [
        "# 对高光谱数据 X 应用 PCA 变换\n",
        "def applyPCA(X, numComponents):\n",
        "    newX = np.reshape(X, (-1, X.shape[2]))\n",
        "    pca = PCA(n_components=numComponents, whiten=True)\n",
        "    newX = pca.fit_transform(newX)\n",
        "    newX = np.reshape(newX, (X.shape[0], X.shape[1], numComponents))\n",
        "    return newX\n",
        "\n",
        "# 对单个像素周围提取 patch 时，边缘像素就无法取了，因此，给这部分像素进行 padding 操作\n",
        "def padWithZeros(X, margin=2):\n",
        "    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))\n",
        "    x_offset = margin\n",
        "    y_offset = margin\n",
        "    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n",
        "    return newX\n",
        "\n",
        "# 在每个像素周围提取 patch ，然后创建成符合 keras 处理的格式\n",
        "def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    # 给 X 做 padding\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = padWithZeros(X, margin=margin)\n",
        "    # split patches\n",
        "    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))\n",
        "    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))\n",
        "    patchIndex = 0\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]\n",
        "            patchesData[patchIndex, :, :, :] = patch\n",
        "            patchesLabels[patchIndex] = y[r-margin, c-margin]\n",
        "            patchIndex = patchIndex + 1\n",
        "    if removeZeroLabels:\n",
        "        patchesData = patchesData[patchesLabels>0,:,:,:]\n",
        "        patchesLabels = patchesLabels[patchesLabels>0]\n",
        "        patchesLabels -= 1\n",
        "    return patchesData, patchesLabels\n",
        "\n",
        "def splitTrainTestSet(X, y, testRatio, randomState=345):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "# 地物类别\n",
        "class_num = 16\n",
        "X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']\n",
        "y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']\n",
        "\n",
        "# 用于测试样本的比例\n",
        "test_ratio = 0.90\n",
        "# 每个像素周围提取 patch 的尺寸\n",
        "patch_size = 25\n",
        "# 使用 PCA 降维，得到主成分的数量\n",
        "pca_components = 30\n",
        "\n",
        "print('Hyperspectral data shape: ', X.shape)\n",
        "print('Label shape: ', y.shape)\n",
        "\n",
        "print('\\n... ... PCA tranformation ... ...')\n",
        "X_pca = applyPCA(X, numComponents=pca_components)\n",
        "print('Data shape after PCA: ', X_pca.shape)\n",
        "\n",
        "print('\\n... ... create data cubes ... ...')\n",
        "X_pca, y = createImageCubes(X_pca, y, windowSize=patch_size)\n",
        "print('Data cube X shape: ', X_pca.shape)\n",
        "print('Data cube y shape: ', y.shape)\n",
        "\n",
        "print('\\n... ... create train & test data ... ...')\n",
        "Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X_pca, y, test_ratio)\n",
        "print('Xtrain shape: ', Xtrain.shape)\n",
        "print('Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "# 改变 Xtrain, Ytrain 的形状，以符合 keras 的要求\n",
        "Xtrain = Xtrain.reshape(-1, patch_size, patch_size, pca_components, 1)\n",
        "Xtest  = Xtest.reshape(-1, patch_size, patch_size, pca_components, 1)\n",
        "print('before transpose: Xtrain shape: ', Xtrain.shape)\n",
        "print('before transpose: Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "# 为了适应 pytorch 结构，数据要做 transpose\n",
        "Xtrain = Xtrain.transpose(0, 4, 3, 1, 2)\n",
        "Xtest  = Xtest.transpose(0, 4, 3, 1, 2)\n",
        "print('after transpose: Xtrain shape: ', Xtrain.shape)\n",
        "print('after transpose: Xtest  shape: ', Xtest.shape)\n",
        "\n",
        "\n",
        "\"\"\" Training dataset\"\"\"\n",
        "class TrainDS(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        self.len = Xtrain.shape[0]\n",
        "        self.x_data = torch.FloatTensor(Xtrain)\n",
        "        self.y_data = torch.LongTensor(ytrain)\n",
        "    def __getitem__(self, index):\n",
        "        # 根据索引返回数据和对应的标签\n",
        "        return self.x_data[index], self.y_data[index]\n",
        "    def __len__(self):\n",
        "        # 返回文件数据的数目\n",
        "        return self.len\n",
        "\n",
        "\"\"\" Testing dataset\"\"\"\n",
        "class TestDS(torch.utils.data.Dataset):\n",
        "    def __init__(self):\n",
        "        self.len = Xtest.shape[0]\n",
        "        self.x_data = torch.FloatTensor(Xtest)\n",
        "        self.y_data = torch.LongTensor(ytest)\n",
        "    def __getitem__(self, index):\n",
        "        # 根据索引返回数据和对应的标签\n",
        "        return self.x_data[index], self.y_data[index]\n",
        "    def __len__(self):\n",
        "        # 返回文件数据的数目\n",
        "        return self.len\n",
        "\n",
        "# 创建 trainloader 和 testloader\n",
        "trainset = TrainDS()\n",
        "testset  = TestDS()\n",
        "train_loader = torch.utils.data.DataLoader(dataset=trainset, batch_size=128, shuffle=True, num_workers=2)\n",
        "test_loader  = torch.utils.data.DataLoader(dataset=testset,  batch_size=128, shuffle=False, num_workers=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boZZ9nlBzUgJ"
      },
      "source": [
        "准备训练环境"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kS6HWZYPys9h"
      },
      "outputs": [],
      "source": [
        "# 使用GPU训练，可以在菜单 \"代码执行工具\" -> \"更改运行时类型\" 里进行设置\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# 网络放到GPU上\n",
        "net = HybridSN().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.001)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vnJqPCNpzYtl"
      },
      "source": [
        "开始训练，保存训练过程的损失下降和模型(无Dropout控制)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nVd8zcr17XvT",
        "outputId": "da08db80-dc2a-4048-da6f-140bbe7345a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[Epoch: 1]   [loss avg: 20.4506]   [current loss: 20.4506]\n",
            "[Epoch: 2]   [loss avg: 19.5083]   [current loss: 18.5659]\n",
            "[Epoch: 3]   [loss avg: 18.8121]   [current loss: 17.4198]\n",
            "[Epoch: 4]   [loss avg: 17.9139]   [current loss: 15.2194]\n",
            "[Epoch: 5]   [loss avg: 16.8124]   [current loss: 12.4062]\n",
            "[Epoch: 6]   [loss avg: 15.6728]   [current loss: 9.9746]\n",
            "[Epoch: 7]   [loss avg: 14.5622]   [current loss: 7.8987]\n",
            "[Epoch: 8]   [loss avg: 13.5095]   [current loss: 6.1407]\n",
            "[Epoch: 9]   [loss avg: 12.5119]   [current loss: 4.5313]\n",
            "[Epoch: 10]   [loss avg: 11.5749]   [current loss: 3.1417]\n",
            "[Epoch: 11]   [loss avg: 10.7467]   [current loss: 2.4649]\n",
            "[Epoch: 12]   [loss avg: 10.0073]   [current loss: 1.8738]\n",
            "[Epoch: 13]   [loss avg: 9.3607]   [current loss: 1.6019]\n",
            "[Epoch: 14]   [loss avg: 8.7711]   [current loss: 1.1061]\n",
            "[Epoch: 15]   [loss avg: 8.2536]   [current loss: 1.0078]\n",
            "[Epoch: 16]   [loss avg: 7.7820]   [current loss: 0.7083]\n",
            "[Epoch: 17]   [loss avg: 7.3673]   [current loss: 0.7324]\n",
            "[Epoch: 18]   [loss avg: 6.9967]   [current loss: 0.6968]\n",
            "[Epoch: 19]   [loss avg: 6.6711]   [current loss: 0.8105]\n",
            "[Epoch: 20]   [loss avg: 6.3617]   [current loss: 0.4819]\n",
            "[Epoch: 21]   [loss avg: 6.0875]   [current loss: 0.6044]\n",
            "[Epoch: 22]   [loss avg: 5.8281]   [current loss: 0.3799]\n",
            "[Epoch: 23]   [loss avg: 5.5912]   [current loss: 0.3805]\n",
            "[Epoch: 24]   [loss avg: 5.3715]   [current loss: 0.3187]\n",
            "[Epoch: 25]   [loss avg: 5.1707]   [current loss: 0.3498]\n",
            "[Epoch: 26]   [loss avg: 4.9845]   [current loss: 0.3301]\n",
            "[Epoch: 27]   [loss avg: 4.8113]   [current loss: 0.3074]\n",
            "[Epoch: 28]   [loss avg: 4.6457]   [current loss: 0.1743]\n",
            "[Epoch: 29]   [loss avg: 4.4944]   [current loss: 0.2596]\n",
            "[Epoch: 30]   [loss avg: 4.3546]   [current loss: 0.3015]\n",
            "[Epoch: 31]   [loss avg: 4.2261]   [current loss: 0.3687]\n",
            "[Epoch: 32]   [loss avg: 4.0986]   [current loss: 0.1480]\n",
            "[Epoch: 33]   [loss avg: 3.9799]   [current loss: 0.1804]\n",
            "[Epoch: 34]   [loss avg: 3.8748]   [current loss: 0.4079]\n",
            "[Epoch: 35]   [loss avg: 3.7736]   [current loss: 0.3302]\n",
            "[Epoch: 36]   [loss avg: 3.6761]   [current loss: 0.2634]\n",
            "[Epoch: 37]   [loss avg: 3.5859]   [current loss: 0.3391]\n",
            "[Epoch: 38]   [loss avg: 3.4986]   [current loss: 0.2681]\n",
            "[Epoch: 39]   [loss avg: 3.4135]   [current loss: 0.1803]\n",
            "[Epoch: 40]   [loss avg: 3.3326]   [current loss: 0.1800]\n",
            "[Epoch: 41]   [loss avg: 3.2552]   [current loss: 0.1565]\n",
            "[Epoch: 42]   [loss avg: 3.1797]   [current loss: 0.0838]\n",
            "[Epoch: 43]   [loss avg: 3.1087]   [current loss: 0.1294]\n",
            "[Epoch: 44]   [loss avg: 3.0455]   [current loss: 0.3269]\n",
            "[Epoch: 45]   [loss avg: 2.9802]   [current loss: 0.1053]\n",
            "[Epoch: 46]   [loss avg: 2.9194]   [current loss: 0.1842]\n",
            "[Epoch: 47]   [loss avg: 2.8585]   [current loss: 0.0565]\n",
            "[Epoch: 48]   [loss avg: 2.8016]   [current loss: 0.1288]\n",
            "[Epoch: 49]   [loss avg: 2.7469]   [current loss: 0.1199]\n",
            "[Epoch: 50]   [loss avg: 2.6960]   [current loss: 0.2053]\n",
            "[Epoch: 51]   [loss avg: 2.6457]   [current loss: 0.1307]\n",
            "[Epoch: 52]   [loss avg: 2.5996]   [current loss: 0.2459]\n",
            "[Epoch: 53]   [loss avg: 2.5522]   [current loss: 0.0892]\n",
            "[Epoch: 54]   [loss avg: 2.5067]   [current loss: 0.0947]\n",
            "[Epoch: 55]   [loss avg: 2.4628]   [current loss: 0.0906]\n",
            "[Epoch: 56]   [loss avg: 2.4192]   [current loss: 0.0197]\n",
            "[Epoch: 57]   [loss avg: 2.3776]   [current loss: 0.0510]\n",
            "[Epoch: 58]   [loss avg: 2.3379]   [current loss: 0.0747]\n",
            "[Epoch: 59]   [loss avg: 2.2992]   [current loss: 0.0536]\n",
            "[Epoch: 60]   [loss avg: 2.2611]   [current loss: 0.0115]\n",
            "[Epoch: 61]   [loss avg: 2.2253]   [current loss: 0.0769]\n",
            "[Epoch: 62]   [loss avg: 2.1924]   [current loss: 0.1906]\n",
            "[Epoch: 63]   [loss avg: 2.1583]   [current loss: 0.0393]\n",
            "[Epoch: 64]   [loss avg: 2.1272]   [current loss: 0.1720]\n",
            "[Epoch: 65]   [loss avg: 2.0961]   [current loss: 0.1037]\n",
            "[Epoch: 66]   [loss avg: 2.0673]   [current loss: 0.1928]\n",
            "[Epoch: 67]   [loss avg: 2.0379]   [current loss: 0.1033]\n",
            "[Epoch: 68]   [loss avg: 2.0086]   [current loss: 0.0413]\n",
            "[Epoch: 69]   [loss avg: 1.9812]   [current loss: 0.1210]\n",
            "[Epoch: 70]   [loss avg: 1.9538]   [current loss: 0.0632]\n",
            "[Epoch: 71]   [loss avg: 1.9279]   [current loss: 0.1095]\n",
            "[Epoch: 72]   [loss avg: 1.9017]   [current loss: 0.0463]\n",
            "[Epoch: 73]   [loss avg: 1.8767]   [current loss: 0.0757]\n",
            "[Epoch: 74]   [loss avg: 1.8520]   [current loss: 0.0481]\n",
            "[Epoch: 75]   [loss avg: 1.8281]   [current loss: 0.0619]\n",
            "[Epoch: 76]   [loss avg: 1.8048]   [current loss: 0.0561]\n",
            "[Epoch: 77]   [loss avg: 1.7816]   [current loss: 0.0190]\n",
            "[Epoch: 78]   [loss avg: 1.7594]   [current loss: 0.0492]\n",
            "[Epoch: 79]   [loss avg: 1.7408]   [current loss: 0.2891]\n",
            "[Epoch: 80]   [loss avg: 1.7205]   [current loss: 0.1196]\n",
            "[Epoch: 81]   [loss avg: 1.6995]   [current loss: 0.0160]\n",
            "[Epoch: 82]   [loss avg: 1.6797]   [current loss: 0.0728]\n",
            "[Epoch: 83]   [loss avg: 1.6596]   [current loss: 0.0145]\n",
            "[Epoch: 84]   [loss avg: 1.6403]   [current loss: 0.0410]\n",
            "[Epoch: 85]   [loss avg: 1.6212]   [current loss: 0.0125]\n",
            "[Epoch: 86]   [loss avg: 1.6025]   [current loss: 0.0176]\n",
            "[Epoch: 87]   [loss avg: 1.5846]   [current loss: 0.0437]\n",
            "[Epoch: 88]   [loss avg: 1.5667]   [current loss: 0.0115]\n",
            "[Epoch: 89]   [loss avg: 1.5493]   [current loss: 0.0165]\n",
            "[Epoch: 90]   [loss avg: 1.5327]   [current loss: 0.0498]\n",
            "[Epoch: 91]   [loss avg: 1.5160]   [current loss: 0.0175]\n",
            "[Epoch: 92]   [loss avg: 1.5002]   [current loss: 0.0662]\n",
            "[Epoch: 93]   [loss avg: 1.4855]   [current loss: 0.1316]\n",
            "[Epoch: 94]   [loss avg: 1.4704]   [current loss: 0.0667]\n",
            "[Epoch: 95]   [loss avg: 1.4551]   [current loss: 0.0185]\n",
            "[Epoch: 96]   [loss avg: 1.4404]   [current loss: 0.0350]\n",
            "[Epoch: 97]   [loss avg: 1.4277]   [current loss: 0.2100]\n",
            "[Epoch: 98]   [loss avg: 1.4146]   [current loss: 0.1444]\n",
            "[Epoch: 99]   [loss avg: 1.4008]   [current loss: 0.0524]\n",
            "[Epoch: 100]   [loss avg: 1.3886]   [current loss: 0.1784]\n",
            "[Epoch: 101]   [loss avg: 1.3762]   [current loss: 0.1413]\n",
            "[Epoch: 102]   [loss avg: 1.3635]   [current loss: 0.0809]\n",
            "[Epoch: 103]   [loss avg: 1.3516]   [current loss: 0.1367]\n",
            "[Epoch: 104]   [loss avg: 1.3397]   [current loss: 0.1120]\n",
            "[Epoch: 105]   [loss avg: 1.3280]   [current loss: 0.1097]\n",
            "[Epoch: 106]   [loss avg: 1.3167]   [current loss: 0.1257]\n",
            "[Epoch: 107]   [loss avg: 1.3054]   [current loss: 0.1116]\n",
            "[Epoch: 108]   [loss avg: 1.2941]   [current loss: 0.0904]\n",
            "[Epoch: 109]   [loss avg: 1.2842]   [current loss: 0.2125]\n",
            "[Epoch: 110]   [loss avg: 1.2748]   [current loss: 0.2466]\n",
            "[Epoch: 111]   [loss avg: 1.2664]   [current loss: 0.3482]\n",
            "[Epoch: 112]   [loss avg: 1.2585]   [current loss: 0.3789]\n",
            "[Epoch: 113]   [loss avg: 1.2501]   [current loss: 0.3051]\n",
            "[Epoch: 114]   [loss avg: 1.2428]   [current loss: 0.4203]\n",
            "[Epoch: 115]   [loss avg: 1.2331]   [current loss: 0.1276]\n",
            "[Epoch: 116]   [loss avg: 1.2239]   [current loss: 0.1604]\n",
            "[Epoch: 117]   [loss avg: 1.2152]   [current loss: 0.2083]\n",
            "[Epoch: 118]   [loss avg: 1.2055]   [current loss: 0.0733]\n",
            "[Epoch: 119]   [loss avg: 1.1958]   [current loss: 0.0513]\n",
            "[Epoch: 120]   [loss avg: 1.1860]   [current loss: 0.0185]\n",
            "[Epoch: 121]   [loss avg: 1.1766]   [current loss: 0.0483]\n",
            "[Epoch: 122]   [loss avg: 1.1672]   [current loss: 0.0341]\n",
            "[Epoch: 123]   [loss avg: 1.1590]   [current loss: 0.1555]\n",
            "[Epoch: 124]   [loss avg: 1.1508]   [current loss: 0.1390]\n",
            "[Epoch: 125]   [loss avg: 1.1420]   [current loss: 0.0597]\n",
            "[Epoch: 126]   [loss avg: 1.1337]   [current loss: 0.0909]\n",
            "[Epoch: 127]   [loss avg: 1.1252]   [current loss: 0.0579]\n",
            "[Epoch: 128]   [loss avg: 1.1169]   [current loss: 0.0560]\n",
            "[Epoch: 129]   [loss avg: 1.1085]   [current loss: 0.0399]\n",
            "[Epoch: 130]   [loss avg: 1.1001]   [current loss: 0.0156]\n",
            "[Epoch: 131]   [loss avg: 1.0929]   [current loss: 0.1520]\n",
            "[Epoch: 132]   [loss avg: 1.0852]   [current loss: 0.0790]\n",
            "[Epoch: 133]   [loss avg: 1.0774]   [current loss: 0.0496]\n",
            "[Epoch: 134]   [loss avg: 1.0696]   [current loss: 0.0311]\n",
            "[Epoch: 135]   [loss avg: 1.0621]   [current loss: 0.0530]\n",
            "[Epoch: 136]   [loss avg: 1.0545]   [current loss: 0.0260]\n",
            "[Epoch: 137]   [loss avg: 1.0468]   [current loss: 0.0108]\n",
            "[Epoch: 138]   [loss avg: 1.0398]   [current loss: 0.0811]\n",
            "[Epoch: 139]   [loss avg: 1.0325]   [current loss: 0.0263]\n",
            "[Epoch: 140]   [loss avg: 1.0271]   [current loss: 0.2720]\n",
            "[Epoch: 141]   [loss avg: 1.0204]   [current loss: 0.0835]\n",
            "[Epoch: 142]   [loss avg: 1.0141]   [current loss: 0.1281]\n",
            "[Epoch: 143]   [loss avg: 1.0084]   [current loss: 0.1897]\n",
            "[Epoch: 144]   [loss avg: 1.0021]   [current loss: 0.1063]\n",
            "[Epoch: 145]   [loss avg: 0.9956]   [current loss: 0.0632]\n",
            "[Epoch: 146]   [loss avg: 0.9891]   [current loss: 0.0461]\n",
            "[Epoch: 147]   [loss avg: 0.9844]   [current loss: 0.2869]\n",
            "[Epoch: 148]   [loss avg: 0.9783]   [current loss: 0.0809]\n",
            "[Epoch: 149]   [loss avg: 0.9724]   [current loss: 0.1023]\n",
            "[Epoch: 150]   [loss avg: 0.9667]   [current loss: 0.1197]\n",
            "[Epoch: 151]   [loss avg: 0.9608]   [current loss: 0.0773]\n",
            "[Epoch: 152]   [loss avg: 0.9556]   [current loss: 0.1696]\n",
            "[Epoch: 153]   [loss avg: 0.9496]   [current loss: 0.0406]\n",
            "[Epoch: 154]   [loss avg: 0.9440]   [current loss: 0.0810]\n",
            "[Epoch: 155]   [loss avg: 0.9387]   [current loss: 0.1207]\n",
            "[Epoch: 156]   [loss avg: 0.9332]   [current loss: 0.0919]\n",
            "[Epoch: 157]   [loss avg: 0.9279]   [current loss: 0.0974]\n",
            "[Epoch: 158]   [loss avg: 0.9233]   [current loss: 0.1997]\n",
            "[Epoch: 159]   [loss avg: 0.9177]   [current loss: 0.0253]\n",
            "[Epoch: 160]   [loss avg: 0.9123]   [current loss: 0.0553]\n",
            "[Epoch: 161]   [loss avg: 0.9067]   [current loss: 0.0245]\n",
            "[Epoch: 162]   [loss avg: 0.9018]   [current loss: 0.1062]\n",
            "[Epoch: 163]   [loss avg: 0.8963]   [current loss: 0.0059]\n",
            "[Epoch: 164]   [loss avg: 0.8913]   [current loss: 0.0827]\n",
            "[Epoch: 165]   [loss avg: 0.8860]   [current loss: 0.0094]\n",
            "[Epoch: 166]   [loss avg: 0.8812]   [current loss: 0.0814]\n",
            "[Epoch: 167]   [loss avg: 0.8760]   [current loss: 0.0201]\n",
            "[Epoch: 168]   [loss avg: 0.8710]   [current loss: 0.0306]\n",
            "[Epoch: 169]   [loss avg: 0.8664]   [current loss: 0.1017]\n",
            "[Epoch: 170]   [loss avg: 0.8617]   [current loss: 0.0574]\n",
            "[Epoch: 171]   [loss avg: 0.8577]   [current loss: 0.1855]\n",
            "[Epoch: 172]   [loss avg: 0.8530]   [current loss: 0.0553]\n",
            "[Epoch: 173]   [loss avg: 0.8489]   [current loss: 0.1314]\n",
            "[Epoch: 174]   [loss avg: 0.8445]   [current loss: 0.0884]\n",
            "[Epoch: 175]   [loss avg: 0.8403]   [current loss: 0.1148]\n",
            "[Epoch: 176]   [loss avg: 0.8364]   [current loss: 0.1543]\n",
            "[Epoch: 177]   [loss avg: 0.8326]   [current loss: 0.1582]\n",
            "[Epoch: 178]   [loss avg: 0.8287]   [current loss: 0.1402]\n",
            "[Epoch: 179]   [loss avg: 0.8242]   [current loss: 0.0173]\n",
            "[Epoch: 180]   [loss avg: 0.8211]   [current loss: 0.2748]\n",
            "[Epoch: 181]   [loss avg: 0.8169]   [current loss: 0.0503]\n",
            "[Epoch: 182]   [loss avg: 0.8133]   [current loss: 0.1737]\n",
            "[Epoch: 183]   [loss avg: 0.8109]   [current loss: 0.3641]\n",
            "[Epoch: 184]   [loss avg: 0.8087]   [current loss: 0.4118]\n",
            "[Epoch: 185]   [loss avg: 0.8048]   [current loss: 0.0809]\n",
            "[Epoch: 186]   [loss avg: 0.8007]   [current loss: 0.0531]\n",
            "[Epoch: 187]   [loss avg: 0.7966]   [current loss: 0.0193]\n",
            "[Epoch: 188]   [loss avg: 0.7925]   [current loss: 0.0325]\n",
            "[Epoch: 189]   [loss avg: 0.7883]   [current loss: 0.0061]\n",
            "[Epoch: 190]   [loss avg: 0.7843]   [current loss: 0.0167]\n",
            "[Epoch: 191]   [loss avg: 0.7803]   [current loss: 0.0235]\n",
            "[Epoch: 192]   [loss avg: 0.7763]   [current loss: 0.0084]\n",
            "[Epoch: 193]   [loss avg: 0.7723]   [current loss: 0.0092]\n",
            "[Epoch: 194]   [loss avg: 0.7684]   [current loss: 0.0113]\n",
            "[Epoch: 195]   [loss avg: 0.7651]   [current loss: 0.1285]\n",
            "[Epoch: 196]   [loss avg: 0.7614]   [current loss: 0.0389]\n",
            "[Epoch: 197]   [loss avg: 0.7576]   [current loss: 0.0093]\n",
            "[Epoch: 198]   [loss avg: 0.7538]   [current loss: 0.0189]\n",
            "[Epoch: 199]   [loss avg: 0.7502]   [current loss: 0.0404]\n",
            "[Epoch: 200]   [loss avg: 0.7466]   [current loss: 0.0174]\n",
            "Finished Training\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 记录每次epoch的平均训练损失\n",
        "train_losses_avg = []\n",
        "# 记录每次epoch的当前损失\n",
        "train_losses_current = []\n",
        "# 开始训练\n",
        "net.train()\n",
        "total_loss = 0\n",
        "for epoch in range(200):\n",
        "    current_loss = 0\n",
        "    for i, (inputs, labels) in enumerate(train_loader):\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # 优化器梯度归零\n",
        "        optimizer.zero_grad()\n",
        "        # 正向传播 +　反向传播 + 优化\n",
        "        outputs = net(inputs)\n",
        "\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "        current_loss += loss.item()\n",
        "    train_losses_avg.append(total_loss / (epoch+1))\n",
        "    train_losses_current.append(current_loss)\n",
        "    print('[Epoch: %d]   [loss avg: %.4f]   [current loss: %.4f]' %(epoch + 1, total_loss/(epoch+1), current_loss))\n",
        "\n",
        "print('Finished Training')\n",
        "\n",
        "# 可视化训练损失\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses_avg, label='Average Loss per Epoch')\n",
        "plt.plot(train_losses_current, label='Current Loss per Epoch')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss per Epoch')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "# 保存图像\n",
        "plt.savefig(f'training_loss_NoSE_NoSoftmax_NoDropoutControl.png')\n",
        "\n",
        "# 显示图像\n",
        "plt.show()\n",
        "\n",
        "# 保存训练后的模型\n",
        "torch.save(net.state_dict(), 'HybirdSN_NoSE_NoSoftmax_NoDropoutControl.pth')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZkcouFbsMsb"
      },
      "source": [
        "模型测试，并可视化准确率"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871
        },
        "id": "Sl7BFxStsNRd",
        "outputId": "a8edfb88-2e32-4561-9ebc-ca47ea8a3810"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0     0.9750    0.9512    0.9630        41\n",
            "         1.0     0.9886    0.9455    0.9666      1285\n",
            "         2.0     0.9879    0.9813    0.9846       747\n",
            "         3.0     0.9952    0.9765    0.9858       213\n",
            "         4.0     0.9620    0.9885    0.9751       435\n",
            "         5.0     0.9834    0.9909    0.9871       657\n",
            "         6.0     1.0000    1.0000    1.0000        25\n",
            "         7.0     1.0000    1.0000    1.0000       430\n",
            "         8.0     0.8000    0.6667    0.7273        18\n",
            "         9.0     0.9942    0.9806    0.9873       875\n",
            "        10.0     0.9665    0.9910    0.9786      2210\n",
            "        11.0     0.9665    0.9719    0.9692       534\n",
            "        12.0     0.9890    0.9730    0.9809       185\n",
            "        13.0     0.9913    0.9974    0.9943      1139\n",
            "        14.0     0.9769    0.9769    0.9769       347\n",
            "        15.0     0.9176    0.9286    0.9231        84\n",
            "\n",
            "    accuracy                         0.9803      9225\n",
            "   macro avg     0.9684    0.9575    0.9625      9225\n",
            "weighted avg     0.9804    0.9803    0.9802      9225\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 控制第一次迭代时是否初始化预测结果数组\n",
        "count = 0\n",
        "# 记录测试集中分类正确的样本数量\n",
        "correct_test = 0\n",
        "# 记录测试集中的总样本数量\n",
        "total_test = 0\n",
        "# 记录每个batch的准确率\n",
        "val_accuracies = []\n",
        "\n",
        "# 在测试集上计算准确率并预测结果\n",
        "for inputs, labels in test_loader:\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = net(inputs)\n",
        "\n",
        "    # 获取预测结果\n",
        "    _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "    # 更新测试准确率\n",
        "    correct_test += (predicted == labels).sum().item()\n",
        "    total_test += labels.size(0)\n",
        "\n",
        "    # 将预测结果添加到y_pred_test\n",
        "    outputs = np.argmax(outputs.detach().cpu().numpy(), axis=1)\n",
        "    if count == 0:\n",
        "        y_pred_test = outputs\n",
        "        count = 1\n",
        "    else:\n",
        "        y_pred_test = np.concatenate((y_pred_test, outputs))\n",
        "\n",
        "    # 计算当前batch的准确率\n",
        "    batch_accuracy = (predicted == labels).sum().item() / labels.size(0)\n",
        "    val_accuracies.append(batch_accuracy)\n",
        "\n",
        "# 总准确率\n",
        "test_accuracy = correct_test / total_test\n",
        "\n",
        "# 生成分类报告\n",
        "classification = classification_report(ytest, y_pred_test, digits=4)\n",
        "print(classification)\n",
        "\n",
        "# 可视化准确率\n",
        "plt.plot(val_accuracies, label='Test Accuracy per Batch')\n",
        "plt.axhline(y=test_accuracy, color='r', linestyle='--', label=f'Overall Accuracy: {test_accuracy:.4f}')\n",
        "plt.title('Test Accuracy During Testing')\n",
        "plt.xlabel('Batch Index')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# 保存图像\n",
        "plt.savefig(f'testing_accuracy_NoSE_NoSoftmax_NoDropoutControl.png')\n",
        "\n",
        "# 显示图像\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fdi2E1cvCtO"
      },
      "source": [
        "显示预测类别图例"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeTp3Q45u-4g",
        "outputId": "efca794e-a810-4dbf-8892-9c8985a8d834"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-17-de9a473a6bd4>:24: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  outputs[i][j] = prediction + 1\n"
          ]
        }
      ],
      "source": [
        "# 加载数据\n",
        "X = sio.loadmat('Indian_pines_corrected.mat')['indian_pines_corrected']\n",
        "y = sio.loadmat('Indian_pines_gt.mat')['indian_pines_gt']\n",
        "\n",
        "height = y.shape[0]\n",
        "width = y.shape[1]\n",
        "\n",
        "# 假设已经应用了PCA和填充零\n",
        "X = applyPCA(X, numComponents=pca_components)\n",
        "X = padWithZeros(X, patch_size//2)\n",
        "\n",
        "# 逐像素预测类别\n",
        "outputs = np.zeros((height, width))\n",
        "for i in range(height):\n",
        "    for j in range(width):\n",
        "        if int(y[i, j]) == 0:\n",
        "            continue\n",
        "        else:\n",
        "            image_patch = X[i:i + patch_size, j:j + patch_size, :]\n",
        "            image_patch = image_patch.reshape(1, image_patch.shape[0], image_patch.shape[1], image_patch.shape[2], 1)\n",
        "            X_test_image = torch.FloatTensor(image_patch.transpose(0, 4, 3, 1, 2)).to(device)\n",
        "            prediction = net(X_test_image)\n",
        "            prediction = np.argmax(prediction.detach().cpu().numpy(), axis=1)\n",
        "            outputs[i][j] = prediction + 1\n",
        "\n",
        "# 定义Indian Pines的类别标签\n",
        "target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn',\n",
        "                'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed',\n",
        "                'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill',\n",
        "                'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives',\n",
        "                'Stone-Steel-Towers']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "U2QmgFDL3UU2",
        "outputId": "2e13fe1f-abeb-4336-db4c-f1849631867a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import numpy as np\n",
        "from matplotlib.colors import Normalize\n",
        "\n",
        "# 设置颜色映射表和归一化\n",
        "cmap = plt.cm.Spectral\n",
        "norm = Normalize(vmin=1, vmax=len(target_names))  # 确保vmin和vmax是基于类别数来设定\n",
        "\n",
        "# 显示预测图像，使用 matplotlib 的 imshow 来绘制\n",
        "plt.imshow(outputs, cmap=cmap, norm=norm)\n",
        "plt.colorbar()  # 显示色条\n",
        "plt.title('Predicted Image')\n",
        "\n",
        "# 创建自定义图例\n",
        "patches = [mpatches.Patch(color=cmap(i / len(target_names)), label=label)\n",
        "           for i, label in enumerate(target_names,start=1)]\n",
        "\n",
        "# 显示图例\n",
        "plt.legend(handles=patches, bbox_to_anchor=(1.25, 0.95), loc='upper left', borderaxespad=0.)\n",
        "\n",
        "# 保存图像到本地文件\n",
        "plt.savefig('predicted_image_NoSE_NoSoftmax_NoDropoutControl.png', bbox_inches='tight')  # 保存为PNG文件，'tight' 会自动调整边距\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xyUTBmVm4Zr4"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
